HT: AI inside Immersion PART I — FORESIGHT SNAPSHOT  |  HT: AI Inside Immersion  |  Fixed Time-Stamped Synthesis 2026 HT: AI Inside Immersion Card Type Historical Technology Shift Series Immersive Futures Guild — Vision 2035 Layer 1 — Atomic Foresight Object Status Active Confidence Medium Workshop Circle of Scholars — January 2026 Facilitator Circle of Scholars Workshop Team Tags AI-in-XR  |  historical  |  adaptive  |  layer1  |  ht Tally.so Form https://tally.so/r/ilrn-if-ht-aiimm-2026 The integration of AI capabilities — adaptive engines, natural language interaction, computer vision — directly into XR environments marks a qualitative shift from XR as a presentation medium to XR as an intelligent, responsive environment. Early research on AI-inside-immersion is generating an initial evidence base but the design, governance, and pedagogical implications remain significantly underexplored. Key Drivers / Contributing Conditions: On-device AI processing enabling real-time environmental response Platform integration of LLM APIs into XR development toolkits Early commercial deployments in training, healthcare, and education sectors Tensions Carried Forward to Part II: How should design principles for traditional XR learning be updated for AI-responsive immersive environments? Linked Scenarios / Strands: FT: Agentic AI | STRAND: Human-Centered AI + XR Ways of Knowing: Tree  ·  Garden  ·  Lantern PART II — COMMUNITY EVIDENCE & DIALOGUE TRACK  |  HT: AI Inside Immersion  |  H2 2026 — Living T COMMUNITY CONTRIBUTION FORM  —  HT: AI Inside Immersion Submit case examples, methodological challenges, cultural perspectives, and proposed evidence criteria via: https://tally.so/r/ilrn-if-ht-aiimm-2026 Part II — Scope and Instructions This section collects community responses, case examples, and challenges to the Part I foresight snapshot above. It opens July 1, 2026 and undergoes synthesis review in September 2026, November 2026, and January 2027. Contributions are submitted via the Tally.so form above and appear in the registers below after editorial review. The Part I text is not modified in response to Part II contributions; it is versioned at the Annual Handoff review. Contribution categories:  Case Example  |  Methodological Challenge  |  Cultural/Community Perspective  |  Proposed Evidence Criterion Ways of Knowing accepted:  Tree (evidence)  |  Garden (practice)  |  Lantern (futures) Tensions Open for Community Response: How should design principles for traditional XR learning be updated for AI-responsive immersive environments? Contributor / Date Category Way of Knowing Contribution Summary [ Awaiting contributions — form opens July 1, 2026 ]